1. Field of the Invention
This invention relates to lossless compressive encoding and decoding systems which are capable of accurately regenerating original information which are subjected to compression and expansion, and particularly to compressive encoding and decoding systems which are suited to lossless compressive encoding of audio signals. This application is based on patent application No. Hei 8-332339 filed in Japan, the content of which is incorporated herein by reference.
2. Prior Art
Conventional computer technolgy frequently uses the lossless compressive encoding for compression of data. As the compressive encoding method, it is possible to employ a variety of methods such as the Haffmann coding method and run-length coding method. Those methods use the apperance probability of the bit pattern as well as the correlation. For this reason, an effect of compression is not so high when the compressive encoding method is applied to the bit pattern which, like audio PCM data, is meaningless in a sense of statics.
By the way, the ADPCM method (where `ADPCM` stands for `Adaptive Differential Pulse Code Modulation`) is kown as the data compression method of audio signals. Using high correlation between adjacent sampling values of audio signals, the above method performs quantization on a difference between an input signal and a predictive value so as to reduce a bit rate.
The aforementioned ADPCM method is designed to perform requantization of a difference value by using 4 bits and 16 levels, for example. In the requantization, the method changes quantization step size thereof in an adaptive manner to follow increments and decrements of the difference value. For this reason, some of the difference values may cause occurrence of a quantization error. In other words, the ADPCM method does not work as the complete lossless coding. In general, losses are classified into two kinds of losses, i.e., a loss corresponding to overload distortion and a loss corresponding to granular distortion. Herein, the loss of the overload distortion occurs when the difference value becomes so big to exceed a range of quantization, while the loss of the granular distortion occurs when the difference value cannot be represented by multiples of the quantization step size multiplied by an integral number.